



from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFCvtForImageClassification, TFCvtModel
				from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor

class  snake_case__(_UpperCAmelCase     ):

					"""simple docstring"""






					def 					snake_case	(				self   :      Union[str, Any]	):
									lowercase__	:       List[str]						=	self.config_class(**self.inputs_dict	)
									self.parent.assertTrue(hasattr(lowerCAmelCase__ ,  "embed_dim"	)	)
									self.parent.assertTrue(hasattr(lowerCAmelCase__ ,  "num_heads"	)	)






class  snake_case__:

					"""simple docstring"""






					def __init__(				self   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      int ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=13 ,  SCREAMING_SNAKE_CASE   :      Optional[int]=64 ,  SCREAMING_SNAKE_CASE   :      Union[str, Any]=3 ,  SCREAMING_SNAKE_CASE   :      int=[16, 48, 96] ,  SCREAMING_SNAKE_CASE   :      List[Any]=[1, 3, 6] ,  SCREAMING_SNAKE_CASE   :      List[Any]=[1, 2, 10] ,  SCREAMING_SNAKE_CASE   :      Any=[7, 3, 3] ,  SCREAMING_SNAKE_CASE   :      str=[4, 2, 2] ,  SCREAMING_SNAKE_CASE   :      Dict=[2, 1, 1] ,  SCREAMING_SNAKE_CASE   :      int=[2, 2, 2] ,  SCREAMING_SNAKE_CASE   :      str=[False, False, True] ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=[0.0, 0.0, 0.0] ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=0.02 ,  SCREAMING_SNAKE_CASE   :      Union[str, Any]=1E-1_2 ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=True ,  SCREAMING_SNAKE_CASE   :      List[str]=True ,  SCREAMING_SNAKE_CASE   :      List[Any]=2 ,  ):
									lowercase__	:       Dict						=	parent
									lowercase__	:       str						=	batch_size
									lowercase__	:       Union[str, Any]						=	image_size
									lowercase__	:       Tuple						=	patch_sizes
									lowercase__	:       List[str]						=	patch_stride
									lowercase__	:       List[str]						=	patch_padding
									lowercase__	:       str						=	is_training
									lowercase__	:       Optional[int]						=	use_labels
									lowercase__	:       Dict						=	num_labels
									lowercase__	:       Any						=	num_channels
									lowercase__	:       Optional[Any]						=	embed_dim
									lowercase__	:       Optional[Any]						=	num_heads
									lowercase__	:       Union[str, Any]						=	stride_kv
									lowercase__	:       List[str]						=	depth
									lowercase__	:       Optional[int]						=	cls_token
									lowercase__	:       Tuple						=	attention_drop_rate
									lowercase__	:       int						=	initializer_range
									lowercase__	:       Union[str, Any]						=	layer_norm_eps







					def 					snake_case	(				self   :      int	):
									lowercase__	:       Any						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

									lowercase__	:       str						=	None
									if self.use_labels:
													# create a random int32 tensor of given shape
													lowercase__	:       str						=	ids_tensor([self.batch_size] ,  self.num_labels	)

									lowercase__	:       Union[str, Any]						=	self.get_config()
									return config, pixel_values, labels







					def 					snake_case	(				self   :      Optional[int]	):
									return CvtConfig(
									    image_size=self.image_size ,  num_labels=self.num_labels ,  num_channels=self.num_channels ,  embed_dim=self.embed_dim ,  num_heads=self.num_heads ,  patch_sizes=self.patch_sizes ,  patch_padding=self.patch_padding ,  patch_stride=self.patch_stride ,  stride_kv=self.stride_kv ,  depth=self.depth ,  cls_token=self.cls_token ,  attention_drop_rate=self.attention_drop_rate ,  initializer_range=self.initializer_range ,  )







					def 					snake_case	(				self   :      Tuple ,  SCREAMING_SNAKE_CASE   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      str	):
									lowercase__	:       int						=	TFCvtModel(config=lowerCAmelCase__	)
									lowercase__	:       List[Any]						=	model(lowerCAmelCase__ ,  training=lowerCAmelCase__	)
									lowercase__	:       str						=	(self.image_size, self.image_size)
									lowercase__				,				lowercase__	:       List[str]						=	image_size[0], image_size[1]
									for i in range(len(self.depth	)	):
													lowercase__	:       Tuple						=	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
													lowercase__	:       str						=	floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
									self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.embed_dim[-1], height, width)	)







					def 					snake_case	(				self   :      List[str] ,  SCREAMING_SNAKE_CASE   :      int ,  SCREAMING_SNAKE_CASE   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      Union[str, Any]	):
									lowercase__	:       Optional[Any]						=	self.num_labels
									lowercase__	:       List[str]						=	TFCvtForImageClassification(lowerCAmelCase__	)
									lowercase__	:       Optional[Any]						=	model(lowerCAmelCase__ ,  labels=lowerCAmelCase__ ,  training=lowerCAmelCase__	)
									self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)	)







					def 					snake_case	(				self   :      Any	):
									lowercase__	:       List[Any]						=	self.prepare_config_and_inputs()
									lowercase__				,				lowercase__				,				lowercase__	:       int						=	config_and_inputs
									lowercase__	:       int						=	{"pixel_values": pixel_values}
									return config, inputs_dict






@require_tf
class  snake_case__(_UpperCAmelCase							,     _UpperCAmelCase							,     unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					lowercase_								=						(
					    {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
					    if is_tf_available()
					    else {}
					)
					lowercase_								=						False
					lowercase_								=						False
					lowercase_								=						False
					lowercase_								=						False
					lowercase_								=						False







					def 					snake_case	(				self   :      List[str]	):
									lowercase__	:       Optional[int]						=	TFCvtModelTester(self	)
									lowercase__	:       int						=	TFCvtConfigTester(self ,  config_class=lowerCAmelCase__ ,  has_text_modality=lowerCAmelCase__ ,  hidden_size=37	)







					def 					snake_case	(				self   :      Tuple	):
									self.config_tester.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







					@unittest.skip(reason="Cvt does not output attentions"	)
					def 					snake_case	(				self   :      Tuple	):
									pass







					@unittest.skip(reason="Cvt does not use inputs_embeds"	)
					def 					snake_case	(				self   :      Optional[Any]	):
									pass







					@unittest.skip(reason="Cvt does not support input and output embeddings"	)
					def 					snake_case	(				self   :      Optional[int]	):
									pass







					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0 ,  reason="TF does not support backprop for grouped convolutions on CPU." ,  )
					def 					snake_case	(				self   :      Any	):
									super().test_dataset_conversion()







					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0 ,  reason="TF does not support backprop for grouped convolutions on CPU." ,  )
					@slow
					def 					snake_case	(				self   :      Union[str, Any]	):
									super().test_keras_fit()







					@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"	)
					def 					snake_case	(				self   :      Any	):
									lowercase__	:       Union[str, Any]						=	tf.keras.mixed_precision.Policy("mixed_float16"	)
									tf.keras.mixed_precision.set_global_policy(lowerCAmelCase__	)
									super().test_keras_fit()
									tf.keras.mixed_precision.set_global_policy("float32"	)







					def 					snake_case	(				self   :      Union[str, Any]	):
									lowercase__				,				lowercase__	:       Optional[Any]						=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__	:       List[Any]						=	model_class(lowerCAmelCase__	)
													lowercase__	:       Union[str, Any]						=	inspect.signature(model.call	)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase__	:       int						=	[*signature.parameters.keys()]

													lowercase__	:       Tuple						=	["pixel_values"]
													self.assertListEqual(arg_names[:1] ,  lowerCAmelCase__	)







					def 					snake_case	(				self   :      Any	):


									def check_hidden_states_output(SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      List[Any]	):
													lowercase__	:       Any						=	model_class(lowerCAmelCase__	)

													lowercase__	:       List[str]						=	model(**self._prepare_for_class(lowerCAmelCase__ ,  lowerCAmelCase__	)	)
													lowercase__	:       int						=	outputs.hidden_states

													lowercase__	:       Dict						=	len(self.model_tester.depth	)
													self.assertEqual(len(lowerCAmelCase__	) ,  lowerCAmelCase__	)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]	) ,  [
													        self.model_tester.embed_dim[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ] ,  )

									lowercase__				,				lowercase__	:       List[Any]						=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__	:       Any						=	True
													check_hidden_states_output(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__	)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase__	:       int						=	True

													check_hidden_states_output(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__	)







					def 					snake_case	(				self   :      str	):
									lowercase__	:       Union[str, Any]						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__	)







					def 					snake_case	(				self   :      Tuple	):
									lowercase__	:       List[str]						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)







					@slow
					def 					snake_case	(				self   :      Dict	):


									for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase__	:       List[str]						=	TFCvtModel.from_pretrained(lowerCAmelCase__	)
													self.assertIsNotNone(lowerCAmelCase__	)






def    __lowerCamelCase   (  ):

				"""simple docstring"""


				lowercase__	:       Optional[int]						=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				return image

@require_tf
@require_vision
class  snake_case__(unittest.TestCase     ):

					"""simple docstring"""






					@cached_property
					def 					snake_case	(				self   :      Dict	):
									return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)







					@slow
					def 					snake_case	(				self   :      str	):
									lowercase__	:       Optional[int]						=	TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

									lowercase__	:       Union[str, Any]						=	self.default_image_processor
									lowercase__	:       Optional[Any]						=	prepare_img()
									lowercase__	:       Any						=	image_processor(images=lowerCAmelCase__ ,  return_tensors="tf"	)

									# forward pass
									lowercase__	:       str						=	model(**lowerCAmelCase__	)

									# verify the logits
									lowercase__	:       str						=	tf.TensorShape((1, 1_000)	)
									self.assertEqual(outputs.logits.shape ,  lowerCAmelCase__	)

									lowercase__	:       Optional[Any]						=	tf.constant([0.9_285, 0.9_015, -0.3_150]	)
									self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() ,  lowerCAmelCase__ ,  atol=1E-4	)	)





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()